Name
Abstract | ||
|---|---|---|
The principal goal of DNS usage mining is the discovery and analysis of patterns in the query behavior of DNS users. In this paper, we develop a unified framework for DNS usage mining based on Clustering analysis of co-occurrence data derived from DNS server query data. Through transforming the raw query data into co-occurrence matrix, user transaction clustering can be applied to discover the user groups according to their similar query behaviors. Using the aggregate usage profile that represents a user cluster and suitable similarity measure, a specific approach for a domain name recommendation engine is shown. For identifying the latent purpose of a domain name, Probabilistic Latent Semantic Analysis (PLSA) is used, which can automatically discover hidden semantic relationships between users and domain names. We demonstrate the effectiveness of our approaches through experiments performed on real-world data sets. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICDIM.2011.6093364 | ICDIM |
Keywords | Field | DocType |
data analysis,statistical analysis,probabilistic latent semantic analysis,data mining,engines,co occurrence matrix,cluster analysis,recommender systems,vectors,internet | Recommender system,Data mining,Data set,Similarity measure,Information retrieval,Computer science,Domain Name System,Probabilistic latent semantic analysis,Cluster analysis,Database transaction,The Internet | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Wu | 1 | 0 | 0.34 |
| Xiaodong Li | 2 | 55 | 3.89 |
| Xin Wang | 3 | 194 | 53.80 |
| BaoPing Yan | 4 | 118 | 22.81 |